Association of Muscle Mass, Muscle Strength, and Muscle Function with Gait Ability Assessed Using Inertial Measurement Unit Sensors in Older Women

Causal relationship between hand grip strength and cognition/dementia risk: a Mendelian randomization study

BACKGROUND

Muscle strength positively correlates with cognitive function, with the bidirectional causal link between hand grip strength and cognition posing a significant but incompletely understood public health challenge. This study aimed to explore the causal relationship between hand grip strength and cognition and its effect on dementia.

METHODS

A two-sample Mendelian randomization analysis used genome-wide significant single nucleotide polymorphisms (SNPs) (P < 5×10-8, at least P < 5×10-6) linked to hand grip strength (right or left), cognition/dementia risk from the IEU Open GWAS project with 42,484 GWAS summary data sets. The primary analysis employed the inverse variance weighted method, while sensitivity analyses were conducted using the weighted mode and MR-Egger. These analyses aimed to assess the causal relationships between hand grip strength and cognition/dementia risk.

RESULTS

The inverse variance weighted (IVW) analysis indicated a directional positive causal effects of hand grip strength on cognition (Left-hand grip strength on cognitive function (OR (95% Cl): 1.23 (1.02-1.48), P = 0.026)/performance (OR (95% Cl): 1.16 (1.04-1.30), P = 0.009); Right-hand grip strength on cognitive function (OR (95% Cl): 1.23 (1.02-1.48), P = 0.031)/performance (OR (95% Cl): 1.10 (1.02-1.19), P = 0.018), with almost no reverse causality between cognitive function/performance and hand grip strength. Based on the results above, we then researched the directional causal effects of hand grip strength on neurodegenerative diseases (like dementia) with cognitive decline as the main clinical manifestation. However, the IVW methods yielded no evidence to support a causal effect of left-hand grip strength on dementia (P > 0.05).

CONCLUSIONS

This MR study indicates a positive directional causal relationship between hand grip strength and cognition, with no observed causal link to dementia. These results hold implications for the development of public health measures and strategies for preventing cognitive decline.

Higher Levels of Serum Leptin Are Linked with a Reduction in Gait Stability: A Sex-Based Association

Gait stability prevents falls and injuries during physical activities. Muscle strength, aging, and co-existing chronic diseases are factors that affect gait stability. Leptin is an adipokine with pro-inflammatory properties. Several reports demonstrated an association between serum leptin and a reduction in muscle strength. Given the above relationships, we hypothesized that serum leptin could be associated with gait stability. To test this, 146 apparently healthy university students were recruited. Data collection involved anthropometric measurements, physical activity (PA) data, gait parameters, and serum leptin levels. A gait instability index was derived from the percentages of double support time and walking asymmetry (WA) collected from smartphones. Females demonstrated higher leptin levels and WA despite a lower body mass index (BMI). Lower PA levels were also observed among females. Leptin levels were negatively correlated with WA, step count, and vigorous PA (p < 0.05). These correlations remained significant following correction for leptin by BMI. Using logistic regression, a higher leptin-to-BMI ratio was associated with high gait instability (OR = 9.97, 95%CI: 4.17-23.84, p < 0.001). After stratification by sex, this association was only evident among females (OR = 6.09, 95%CI: 1.04-35.56, p = 0.045). These findings suggest a sex-based association between serum leptin and gait stability among apparently healthy students.

Gait-Based AI Models for Detecting Sarcopenia and Cognitive Decline Using Sensor Fusion

Background/Objectives: Sarcopenia and cognitive decline (CD) are prevalent in aging populations, impacting functionality and quality of life. The early detection of these diseases is challenging, often relying on in-person screening, which is difficult to implement regularly. This study aims to develop artificial intelligence algorithms based on gait analysis, integrating sensor and computer vision (CV) data, to detect sarcopenia and CD. Methods: A cross-sectional case-control study was conducted involving 42 individuals aged 60 years or older. Participants were classified as having sarcopenia if they met the criteria established by the European Working Group on Sarcopenia in Older People and as having CD if their score in the Mini-Mental State Examination was ≤24 points. Gait patterns were assessed at usual walking speeds using sensors attached to the feet and lumbar region, and CV data were captured using a camera. Several key variables related to gait dynamics were extracted. Finally, machine learning models were developed using these variables to predict sarcopenia and CD. Results: Models based on sensor data, CV data, and a combination of both technologies achieved high predictive accuracy, particularly for CD. The best model for CD achieved an F1-score of 0.914, with a 95% sensitivity and 92% specificity. The combined technologies model for sarcopenia also demonstrated high performance, yielding an F1-score of 0.748 with a 100% sensitivity and 83% specificity. Conclusions: The study demonstrates that gait analysis through sensor and CV fusion can effectively screen for sarcopenia and CD. The multimodal approach enhances model accuracy, potentially supporting early disease detection and intervention in home settings.

Association Between Standard Gait Measures and Anterior Quadriceps Muscle Thickness as Measured by Point of Care Ultrasound (POCUS)

Background: Gait parameters and sarcopenia both predict falls risk among older adults. Our objective was to evaluate whether fast, easy-to-obtain measures of anterior thigh muscle by point of care ultrasound (POCUS) are significantly associated with standard gait measures. Methods: All subjects were referred from ambulatory geriatric medicine clinics at an academic center. Quadriceps muscle thickness was measured by a portable ultrasound device. Gait variables were measured by the patient in comfortable walking shoes walking for six minutes. The primary response variables were gait variables, and the predictor variables were age, biological sex, body mass index, and muscle thickness. Univariate and multivariate regression analyses were performed. Results: A total of 150 participants were recruited from geriatric medicine clinics (65 women, 84 men). Muscle thickness was measured in 149 participants, and the mean (SD) was 1.91 (0.52) (median 1.82 cm, 0.96 to 3.68 cm). Univariate analysis of gait parameters with age showed a statistically significant correlation with gait speed (R2=0.16, P < 0.000), average stride length (R2=0.142, P < 0.000), and average stride velocity (R2=0.182, P < 0.000). Among all the gait variables, average swing time (P = 0.010) and average stance time (P = 0.010) were correlated significantly with muscle thickness. For multivariate analysis with age and gait variables, age was a significant independent variable for all gait variables that were significant in univariate analysis. Conclusion: POCUS showed a significant association with average swing time, average stance time, and step time variability. Although more work needs to be done, POCUS has the potential to be a rapid screening tool for gait assessment.

Sarcopenia screening based on the assessment of gait with inertial measurement units: a systematic review

BACKGROUND

Gait variables assessed by inertial measurement units (IMUs) show promise as screening tools for aging-related diseases like sarcopenia. The main aims of this systematic review were to analyze and synthesize the scientific evidence for screening sarcopenia based on gait variables assessed by IMUs, and also to review articles that investigated which gait variables assessed by IMUs were related to sarcopenia.

METHODS

Six electronic databases (PubMed, SportDiscus, Web of Science, Cochrane Library, Scopus and IEEE Xplore) were searched for journal articles related to gait, IMUs and sarcopenia. The search was conducted until December 5, 2023. Titles, abstracts and full-length texts for studies were screened to be included.

RESULTS

A total of seven articles were finally included in this review. Despite some methodological variability among the included studies, IMUs demonstrated potential as effective tools for detecting sarcopenia when coupled with artificial intelligence (AI) models, which outperformed traditional statistical methods in classification accuracy. The findings suggest that gait variables related to the stance phase such as stance duration, double support time, and variations between feet, are key indicators of sarcopenia.

CONCLUSIONS

IMUs could be useful tools for sarcopenia screening based on gait analysis, specifically when artificial intelligence is used to process the recorded data. However, more development and research in this field is needed to provide an effective screening tool for doctors and health systems.

Machine learning approach to classifying declines of physical function and muscle strength associated with cognitive function in older women: gait characteristics based on three speeds

Background

The aging process is associated with a cognitive and physical declines that affects neuromotor control, memory, executive functions, and motor abilities. Previous studies have made efforts to find biomarkers, utilizing complex factors such as gait as indicators of cognitive and physical health in older adults. However, while gait involves various complex factors, such as attention and the integration of sensory input, cognitive-related motor planning and execution, and the musculoskeletal system, research on biomarkers that simultaneously considers multiple factors is scarce. This study aimed to extract gait features through stepwise regression, based on three speeds, and evaluate the accuracy of machine-learning (ML) models based on the selected features to solve classification problems caused by declines in cognitive function (Cog) and physical function (PF), and in Cog and muscle strength (MS).

Methods

Cognitive assessments, five times sit-to-stand, and handgrip strength were performed to evaluate the Cog, PF, and MS of 198 women aged 65 years or older. For gait assessment, all participants walked along a 19-meter straight path at three speeds [preferred walking speed (PWS), slower walking speed (SWS), and faster walking speed (FWS)]. The extracted gait features based on the three speeds were selected using stepwise regression.

Results

The ML model accuracies were revealed as follows: 91.2% for the random forest model when using all gait features and 91.9% when using the three features (walking speed and coefficient of variation of the left double support phase at FWS and the right double support phase at SWS) selected for the Cog+PF+ and Cog-PF- classification. In addition, support vector machine showed a Cog+MS+ and Cog-MS- classification problem with 93.6% accuracy when using all gait features and two selected features (left step time at PWS and gait asymmetry at SWS).

Conclusion

Our study provides insights into the gait characteristics of older women with decreased Cog, PF, and MS, based on the three walking speeds and ML analysis using selected gait features, and may help improve objective classification and evaluation according to declines in Cog, PF, and MS among older women.

Association of Handgrip Strength with Hip Fracture and Falls in Community-dwelling Middle-aged and Older Adults: A 4-Year Longitudinal Study

OBJECTIVE

Hip fracture and falls are significant health concerns. Handgrip strength (HGS) is closely associated with overall muscle strength and physical health. However, the longitudinal relationship between HGS and the risk of hip fractures and falls remains unclear, particularly regarding gender differences. This longitudinal study aimed to investigate the association between HGS and the risk of hip fracture and falls in individuals aged 45 years and above, considering gender-specific differences over a 4-year period.

METHODS

This study included 10,092 participants (4471 men and 5621 women) aged 45 years and above from the China Health and Retirement Longitudinal Study (CHARLS). Incidents of hip fractures and falls were recorded during a 4-year follow-up, along with various demographic and clinical factors. Participants were categorized into five groups based on their HGS quintiles. Logistic regression models were employed to estimate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) to assess the relationship between HGS and hip fracture/fall risk.

RESULTS

During the 4-year follow-up period, 223 cases of hip fracture (2.2%) and 1831 cases of falls (18.1%) were documented. Notably, higher HGS demonstrated a strong inverse association with the risk of hip fracture in both males and females (p < 0.05). In comparison to the lowest HGS quintile, the adjusted odds ratios (ORs) for hip fracture were 0.46 (0.27-0.78) for the total population, 0.4 (0.19-0.81) for males and 0.48 (0.23-0.98) for females in the highest HGS quintile. Furthermore, a profound and statistically significant negative correlation between HGS and falls was detected (p < 0.05). The adjusted ORs for falls in the highest HGS quintile, compared to the lowest quintile, were 0.62 (0.51-0.76) in the overall population, 0.59 (0.44-0.78) in males, and 0.78 (0.62-0.99) in females.

CONCLUSION

Our findings highlight the significant inverse association between HGS and the risk of hip fracture and falls in both males and females aged 45 years and above. Assessing handgrip strength may serve as a valuable tool for predicting fracture and fall risk.

The role of ankle and knee muscle characteristics in spatiotemporal gait parameters at different walking speeds: A cross-sectional study

BACKGROUND

Understanding the intricate interplay between ankle and knee muscle characteristics and their impact on gait parameters is crucial for enhancing our comprehension of human locomotion, particularly in the context of varying walking speeds among healthy young adults.

RESEARCH QUESTION

The study aimed to identify the relative importance of ankle and knee flexor and extensor muscle characteristics (e.g., strength estimated by peak torque [PT] and rate of torque development [RTD]) in the spatiotemporal gait parameters and variability in self-selected (SSWS) and fast walking speeds (FWS) in healthy young adults.

METHODS

One hundred and thirty-nine adults (75 men - 54% and 64 women - 46%; 29.04 ± 9.55 years) were assessed about their muscle characteristics (PT and RTD by an isokinetic dynamometer) and spatiotemporal gait parameters at different walking speeds (SSWS and FWS by an instrumented walkway).

RESULTS

Data analysis indicated a weak relationship between the PT and RTD of the ankle and knee and spatiotemporal gait parameters and variability in both walking conditions (SSWS: R2 0.14-0.05; FWS: R2 0.40-0.05). The strength of the knee muscles was more relevant when walking at a self-selected speed, while the strength of the ankle muscles played a more prominent role when walking at a fast pace.

SIGNIFICANCE

The findings underscore the critical role of ankle muscles (plantar and dorsiflexors) at fast walking speeds. Therefore, targeted interventions for strength and optimization of these muscles are paramount.

Relationship between Body Composition and Gait Characteristics in Patients with Cerebral Small Vessel Disease

BACKGROUND

This study aims to explore the correlation between body composition, encompassing factors such as muscle mass and fat distribution, and gait performance during both single-task walking (STW) and dual-task walking (DTW) in patients diagnosed with cerebral small vessel disease (CSVD).

METHODS

The data of hospitalized patients diagnosed with CSVD, including cadence, stride time, velocity and stride length, as well as information on variability, asymmetry and coordination during both STW and DTW, were assessed. The number of falls reported by each participant was also assessed.

RESULTS

A total of 95 CSVD patients were assessed, and the results showed that individuals with low appendicular skeletal muscle mass (ASM), which includes both the low ASM group and the combination of low ASM and high body fat (BF) group, had reduced velocity or cadence, shortened stride length, and prolonged stride time across all walking modalities compared to the control group. Only the combination of the low ASM and high BF group exhibited a deterioration in the coefficient of variation (CV) for all basic parameters and the Phase Coordination Index (PCI) compared to the control group across all walking patterns. Conversely, patients in the high BF group displayed a decline in basic parameters, primarily during cognitive DTW. Concurrently, the high BF group showed a significant increase in the CV and the PCI compared to the control group only during cognitive DTW. Furthermore, regardless of gender, both ASM and BF independently correlated with the occurrence of falls.

CONCLUSIONS

CSVD patients with varying body compositions could allocate different levels of attention to their daily walking routines.

Factors associated with balance impairments in the community-dwelling elderly in urban China

BACKGROUND

Identification of factors relevant to balance performance impairments in the elderly population was critical for developing effective interventions and preventions. However, there have been very limited data available based on large scale studies. The present study identified factors that independently contributed to performance impairments in overall balance, domains of static balance, postural stability, and dynamic balance, and individual items.

METHODS

A total of 1984 community-dwelling Chinese elderly from urban areas of Shanghai were recruited. Information on demographic characteristic, exercise, and health status were collected with a face-to-face interview. Balance performances were assessed on site by trained investigators based on the X16 balance testing scale. To identify the effectors, ordinal logistic regression analysis was applied for overall balance, static balance, postural stability, and dynamic balance. Binary logistic regression analysis was used for 16 items.

RESULTS

The community-dwelling elderly residents were aged from 60 to 97 years old. With increases of age, risks of impairments in overall balance increased gradually (ORs from 1.26 to 3.20, all P < 0.01). In the elderly with overweight and obesity, there was higher proportion of balance impairments compared to the elderly with normal BMI (OR = 1.26, P < 0.001). Regular exercise every week was associated with reduced risks of balance impairments (ORs from 0.63 to 0.73, all P < 0.001). Presences with vision lesion (ORs from 1.28 to 1.59, all P < 0.001), moderate hearing impairment (OR = 1.54, P < 0.001), somesthesis dysfunction (ORs from 1.59 to 13.26, all P < 0.001), and cerebrovascular disease (OR = 1.45, P = 0.001) were related to increased risks of balance impairments. Likewise, age, exercise, vision, hearing, somesthesis, and cerebrovascular disease were significantly associated with static balance, postural stability, and dynamic balance. Both overweight and obesity and underweight were associated with higher proportions of dynamic balance impairments. Regular exercise was significantly related to reduced risks of impairments in 15 out of the 16 items.

CONCLUSIONS

In the elderly, age, overweight and obesity, exercise, vision, hearing, somesthesia, and cerebrovascular disease were dominant factors associated with impairments in overall balance, domains of static balance, postural stability, and dynamic balance, and most individual items.

TRIAL REGISTRATION

Not applicable.

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use.

The Influence of Knee Extensor and Ankle Plantar Flexor Strength on Single-Leg Standing Balance in Older Women

Impaired balance is a significant risk factor for falls among older adults. The precise impact of lower-extremity muscles, including the proportion of muscle strength, on the performance of single-leg standing balance tests in older individuals is very interesting. The aim of this study is to examine the correlation between the knee extensor (KE), ankle plantar flexor (AP) muscle strength, and performance in single-leg standing balance tests in older females. Additionally, it aims to evaluate the combined proportion of KE and AP muscle strength in maintaining balance during single-leg standing. A total of 90 older females (mean age 67.83 ± 8.00 years) were recruited. All participants underwent maximum voluntary isometric contraction (MVIC) testing of the KE and AP muscles, as well as single-leg standing balance tests with eyes open (SSEO) and eyes closed (SSEC). To examine the influence of KE and AP muscle strength on balance performance, multiple regression analysis was conducted. Low correlations were found between SSEO and MVIC of KE and AP muscles, but moderate correlations were found with percentage of MVIC to body weight ratio (%MVIC/BW). The best model for SSEO included 0.99 times of the %MVIC/BW of AP and 0.66 times that of KE muscles as independent predictor variables (r = 0.682). In conclusion, AP muscle strength was found to have a greater impact on single-leg standing balance compared with KE muscle strength.

Effects of age, body height, body weight, body mass index and handgrip strength on the trajectory of the plantar pressure stance-phase curve of the gait cycle

The analysis of gait patterns and plantar pressure distributions via insoles is increasingly used to monitor patients and treatment progress, such as recovery after surgeries. Despite the popularity of pedography, also known as baropodography, characteristic effects of anthropometric and other individual parameters on the trajectory of the stance phase curve of the gait cycle have not been previously reported. We hypothesized characteristic changes of age, body height, body weight, body mass index and handgrip strength on the plantar pressure curve trajectory during gait in healthy participants. Thirty-seven healthy women and men with an average age of 43.65 ± 17.59 years were fitted with Moticon OpenGO insoles equipped with 16 pressure sensors each. Data were recorded at a frequency of 100 Hz during walking at 4 km/h on a level treadmill for 1 minute. Data were processed via a custom-made step detection algorithm. The loading and unloading slopes as well as force extrema-based parameters were computed and characteristic correlations with the targeted parameters were identified via multiple linear regression analysis. Age showed a negative correlation with the mean loading slope. Body height correlated with Fmean_load and the loading slope. Body weight and the body mass index correlated with all analyzed parameters, except the loading slope. In addition, handgrip strength correlated with changes in the second half of the stance phase and did not affect the first half, which is likely due to stronger kick-off. However, only up to 46% of the variability can be explained by age, body weight, height, body mass index and hand grip strength. Thus, further factors must affect the trajectory of the gait cycle curve that were not considered in the present analysis. In conclusion, all analyzed measures affect the trajectory of the stance phase curve. When analyzing insole data, it might be useful to correct for the factors that were identified by using the regression coefficients presented in this paper.